There is an explosion of product offerings in the marketplace today. Many new products are introduced each year, and these products have more features and options than ever before. Even old familiar products get new features that make them more complex. Customers are given more choices, but this means that each product must be ordered and built in many different ways. This widening of product offerings comes with a cost that, because of competitive pressures, often cannot be passed on to the customer. Companies are now faced with a realization that they are providing additional variety without a commensurate increase in revenue. The challenge that these companies face is escalating cost, exploding product offerings, and lack of pricing power.
The following simple example illustrates how product configuration explosion can happen. Assume a small tractor has 9 customer features from which a customer can choose. Such features include model, axle, transmission, etc. Each separate feature itself has multiple options from which to choose. For example, assume there are 12 models, 2 types of axles, 4 types of transmissions, and so on. Multiplying the number of choices for each feature (assuming there are an average of four possible options for each features) reveals that there can be more than 250,000 possible tractor configurations. Assume further that the tractor manufacturer also offers financing options and service/warranty packages with each tractor. Assume that there are 5 finance options, with only certain options applying to certain models, and 8 warranty packages, which vary based on the details of the configuration chosen. Combining the 250,000 possible tractor configurations with 5 finance options and 8 warranty packages results in a further explosion to more than 10 million possible “product configurations” for that one product line. As used herein, a product configuration is an instance of the final end product that the customer sees and buys. As shown in the above example, this may include the warranties and services that are added to the physical end-item configuration to define the final offering. Thus, in this example, the tractor manufacturer now has the challenge of sifting through over 10 million possibilities to determine which set of configurations to manufacture and offer to its customers in order to maximize their profitability and best satisfy customer demand.
Although the above example is for tractors, it should be readily apparent that many other products, such as computers, software, automobiles, washers, dryers, restaurant menu items, retail shelf space, and the like, all have large numbers of possible configurations. The problem of product configurations is also true for many types of services, such as cell phone plans, health care plans, HMOs, pension plans, financial packages, mortgage plans, life insurance, mail and delivery services, and the like.
Whether a company offers a complex product or a portfolio of services, or a combination of both, the above example is indicative of the magnitude of the problem. Companies face the on-going challenge of picking the right set and the right number of configurations to offer—preferably, to satisfy the greatest customer demand while maximizing profitability.
The impact of the company's decision about what products or services to offer is felt across the entire enterprise with regard to cost, revenue potential, and customer satisfaction. More specifically, each final product configuration that is offered by a company has an impact on revenue due to how well it satisfies customer demand, and an impact on cost due to the resources and materials required to support it. As the number of product configurations grow, there is an exponential increase in the number of parts, component configurations, subassemblies, suppliers, inventory, work in process, and overhead. This results in increased cost that is sometimes referred to as “cost of complexity.” Such cost of complexity is estimated to be 5 to 20 percent of the total cost of goods sold. At the highest levels, the proliferation of product configurations impact business processes such as planning, sourcing, manufacturing (make), delivery, and post-sale support and service.
From a planning perspective, the challenge in dealing with many product configurations starts with the planning and engineering groups. These groups are being given shorter planning cycles for bringing new products to market. More product configurations mean more design documents, more interactions to worry about, more testing, and more changes. As a result, the initial plans do not accurately reflect the impact of the product configurations across the entire organization. This often leads to engineering rework and expensive delays.
With regard to sourcing, every product configuration has a different bill of materials (BOM). This means that the parts required depend on the actual configurations that are going to be built. But current ERP (Enterprise Resource Planning) and MRP (Material Requirements Planning) systems use representative or standard (or “phantom”) configurations to determine parts requirements. This results in procurement mistakes that lead to excess inventory of some parts and to insufficient stocking of other parts. Often, expedited delivery, at increased expense, is required to obtain parts that are not in stock.
With respect to manufacturing or make, increased numbers of configurations often lead to unrealistic estimates of capacity requirements, resulting in longer lead times, missed due dates, excess capacity buffers, and in some cases plant shut downs. The increased number of end product configurations also results in increased parts and component configurations, which often results in increased error and rework in the manufacturing process. The increased number of configurations further leads to more complex sequencing issues that travel all the way down to the level of the suppliers.
Where representative or standard configurations are used, companies must accommodate for the lack of planning based on actual configurations. To do this plant managers often stock additional parts inventory and work in process inventory. This results in a buildup of inventory at the parts and component level. Again, at a cost to the company.
With respect to delivery, there is the on-going challenge of matching the right configuration with the right customer. As the configurations grow, this typically results in increased inventory of finished goods and spoilage. The additional configurations also demand additional support services of marketing, sales, logistics, and after-sales support. In the services industry, as the number of product offerings grow, there is a growing cost burden due to training sales teams, marketing, and the delivery of the new products into the market. As the number of product configurations grow, the probability of matching the customer to the right product offering drops dramatically. This leads to enormous frustration and confusion on both the sales teams and the customer.
With regard to post-sales support and service, every company has to support its products after the sale has been made. As the number of configurations grows, the requirements for support services grow much faster. In the manufacturing segment, this translates into parts and service, while in the services segment it translates into large teams of customer service staff that must keep up with and provide support for every product configuration the company has sold.
Thus, it should not be surprising that a company's final set of product offerings is one of the strongest driving factors in determining its overall profitability. Further, the impact on costs of configuration “creep” is very high, and companies must periodically attempt to control it. The most typical approach companies take is to streamline configurations on the basis of low sales volume or low margins. Companies also streamline offerings by eliminating features and options. The problem with these approaches is that they typically sacrifice more market share than necessary. The result is reduced revenue and, invariably a company backlash of introducing even more product configurations in an attempt to gain market share.
Streamlining configurations across the enterprise is even further complicated because a complete solution needs to take into account customer demand, all valid product configurations, and economics of make and deliver for the product, component or service. (parts cost, non-parts cost and revenue). If the solution methodology fails to consider any one of these three elements while deriving the optimal set of product configurations, the solution will be incomplete and could cause a negative impact on the company's bottom line. Each of the three mentioned systems is complex in its own right. But, to derive the optimal set of configurations to maximize profitability, all three systems need to be considered simultaneously.
Currently, companies attempt to determine what product configurations to offer manually or with spreadsheets. While such approaches are satisfactory for addressing products with few configurations, manual calculations are of little benefit in identifying the “right” configurations to manufacture or offer when the number of configurations are orders of magnitude greater. Nevertheless, decisions must be made. Periodically, a product group will make a “best guess” as to what product configurations to offer. Because there is currently no known method to represent all the product configurations for a product line, determine the cost and profit of each configuration, and decide on the best choice, companies must rely upon best guesses, luck, and intuition.
Yet a further common practice among manufacturers of configurable products is to keep track of all of the different configurations they have built so far. When an order is received, it is compared to the history of all configurations that have been built in the past. If it does not match any of these historical configurations, it is assigned a new “version/configuration number.” These version numbers are usually assigned sequentially, and each version number accumulates its own set of instances, usually in the form of a set of serial numbers. Each new serial number becomes an instance of some existing version, or else gets a new version number of its own. Thus, the complete set of units that have been built is partitioned into different versions, each of which represents a unique configuration. Typically, after some period of collecting history, there are a few versions with many instances, and a great many versions with only one or two instances.
Other manufacturers, however, do not keep track of versions. They keep a history, by serial number, of all units that have been built, but this history is never actually collapsed into unique versions. These manufacturers have no way of determining the configurations that have been built.
Another problem is the fact that the version number assigned to a product configuration is usually arbitrary. At best, the version number contains only a small amount of useable information about the relationship between configurations—usually, only as regards one or two features. Hence, most version numbers do not provide enough information to determined if two configurations are similar, or very different. A customer who wants configuration X might be satisfied with configuration Y, if configuration X and configuration Y are close enough in some appropriate sense. But closeness cannot be inferred from version numbers.
A crude way to measure the closeness or similarity of two configurations, X and Y, would be to count the number of features that match. In the tractor example: same model, same axle, everything else different, would give a measure of 2. But the meaning of this “2” is uncertain, since not all features are equally important.
Further, merely keeping track of all of the configurations that have been built to date ignores the far larger set of configurations that have never been built. These never-built configurations may be important missed opportunities. One never-built configuration Z may be close to many other configurations with only one or two instances. Building configuration Z, instead of all these unpopular versions, might satisfy the same customers and be far more profitable.
As the above examples illustrate, determining the optimal set of product offerings is a nontrivial task requiring sophisticated tools that can address this challenge on an ongoing basis. For these and many other reasons, there is a need for a system and method for streamlining product configurations across the enterprise taking into account customer demand, all valid product configurations, and economics of make and delivery for the product, component, or service.
There is a need for a system and methods for enabling a manufacturer or service provider to determine which product or service configurations should be manufactured.
There is yet a further need for a system and methods for enabling a manufacturer or service provider to determine how many of each configuration should be built.
There is also a need for a system and methods for enabling a manufacturer or service provider to determine which configurations are profitable or not profitable.
There is a need for a system and methods for enabling a manufacturer or service provider to determine the parts requirements necessary to build the right configurations.
There is a further need for a system and methods for enabling a manufacturer or service provider to determine how many of each part needs to be ordered and kept in stock.
There is a need for a system and methods for enabling a manufacturer or service provider to determine how to standardize product configurations and to determine what such standardized configurations will cost.
There is an additional need for a system and methods for enabling a manufacturer or service provider to determine how many valid configurations there are and how popular each one has been historically and how popular each one is likely to be in the future.
There is yet another need for a system and methods for enabling a manufacturer or service provider to determine when it is profitable to “give away” free upgrades on certain features, for example, to capture greater market share and/or to minimize cost of complexity.
There is a need for a system and methods for enabling a manufacturer or service provider to identify alternate configurations that may be of interest to a customer desiring a different configuration.
There is also a need for a system and methods for enabling a manufacturer or service provider to determine what small set of configurations are likely to satisfy the most customers.
Further, there is a need for a system and methods for enabling a manufacturer or service provider to identify relationships or similarities between various product configurations.
There is an overall need for a system and methods for enabling a manufacturer or service provider to keep track of all the product configurations they offer and to enable the selection of the “best configurations” to satisfy customer demand.
There is a need for a system and methods for enabling a manufacturer or service provider to represent product configurations as points in a multidimensional or n-dimensional space and for a system and methods of using rules to identify or define closeness of various product configurations in order to further define a “configuration space.”
There is yet a further need for a system and methods for enabling a manufacturer or service provider to use mathematical models and algorithms to manipulate such a configuration space and, correspondingly, to answer questions, such as: (i) which 10 (or 20 or 100) configurations would capture the most customer demand? (ii) which 10 configurations would be the most profitable to offer? (iii) if there is an incremental cost of offering additional configurations, then what is the most profitable number to offer, and what configurations are they?
For these and many other reasons, there is a general need for a method of identifying an optimum set of product configurations from a plurality of possible product configurations within a computerized system is disclosed. Each product configuration has a plurality of selectable features and each selectable feature has a plurality of options. The method of this first aspect includes the steps of representing each of the plurality of possible product configurations as an ordered set of dimensions, each selectable feature being represented by one respective dimension of the ordered set, identifying a plurality of valid product configurations as a subset of the possible product configurations, defining configuration neighborhoods that identify at least one valid product configuration captured by another valid product configuration, defining an optimization model to identify the optimum set of valid product configurations based on a desired objective, solving the optimization model, and presenting the optimum set of valid product configurations that satisfy the desired objective.
For these and many other reasons, there is also a general need for a method of identifying an optimum set of product configurations within a computerized system includes the steps of receiving product configuration data representative of a plurality of possible product configurations capable of manufacture by a company, identifying a plurality of selectable features associated with the plurality of possible product configurations, identifying a plurality of options associated with each respective selectable feature, representing each of the plurality of possible product configurations as an ordered array of the selectable features, identifying a plurality of valid product configurations as a subset of the plurality of possible product configurations, defining an optimization model based on achieving a desired objective, solving the optimization model to identify the optimum set of valid product configurations that achieves the desired objective, and presenting the optimum set of valid product configurations to the company.
For these and many other reasons, there is a further general need for a computerized system for identifying an optimum set of product configurations includes a configuration generator for receiving product configuration data, the product configuration data representative of all possible product configurations, each product configuration defined by a plurality of features, each feature having a plurality of options, the configuration generator applying mix-and-match rule to identify a subset of valid product configurations, the configuration generator further representing each of the valid product configurations as an ordered array, a demand simulator for calculating relative demand for each of the valid product configurations, a cost calculator for calculating and associating a cost of manufacture for each of the valid product configurations, a revenue calculator for calculating and associating a revenue potential for each of the valid product configurations, an objective-based modeler for defining an optimization model and for receiving product configuration information from the configuration generator, the demand simulator, the cost calculator, and the revenue calculator, and an optimization engine for solving the optimization model and presenting the optimal set of product configurations and for presenting costs, revenue, and parts needed for the optimal set of product configurations.
The present invention meets one or more of the above-referenced needs as described herein in greater detail.